Koichiro Tamura
Impact in
- Plant Science top 0.01%
- Plant-Microbe Interactions and Immunity
- Plant Virus Research Studies
- Plant Molecular Biology Research
- Parasitology top 0.01%
Papers in
-
- Genomics and Phylogenetic Studies 35
- RNA and protein synthesis mechanisms 6
- Insect Resistance and Genetics 5
- Genetics 41
- Genetic diversity and population structure 28
- Evolution and Genetic Dynamics 17
- Co-authors
- Sudhir Kumar (35 shared papers)Glen Stecher (11 shared papers)M Nei (5 shared papers)Alan Filipski (5 shared papers)Daniel S. Peterson (4 shared papers)Joel T. Dudley (4 shared papers)Daniel G. Peterson (1 shared paper)Michael Li (1 shared paper)
- Journals
- Molecular Biology and Evolution (25 papers)Bioinformatics (5 papers)Biochemical Genetics (3 papers)Genetics (2 papers)BMC Genomics (2 papers)
- Partner nations
- JapanUnited StatesSaudi Arabia
In The Last Decade
Koichiro Tamura
77 papers receiving 206.3k citations
Koichiro Tamura's Hit Papers
Peers
Comparison fields: 5 of 212
- Plant Science 59.9k
- Parasitology 10.1k
- Ecology 40.8k
- Endocrinology 7.5k
- Horticulture 1.4k
Countries citing papers authored by Koichiro Tamura
This map shows the geographic impact of Koichiro Tamura's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Koichiro Tamura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Koichiro Tamura more than expected).
Fields of papers citing papers by Koichiro Tamura
This network shows the impact of papers produced by Koichiro Tamura. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Koichiro Tamura. The network helps show where Koichiro Tamura may publish in the future.
Co-authors
The 25 scholars most cited alongside Koichiro Tamura, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0 Hit paper breakdown → | 2013 | 41180 |
| 2 | MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods Hit paper breakdown → | 2011 | 35766 |
| 3 | MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets Hit paper breakdown → | 2016 | 35571 |
| 4 | MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms Hit paper breakdown → | 2018 | 27909 |
| 5 | MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0 Hit paper breakdown → | 2007 | 26165 |
| 6 | MEGA11: Molecular Evolutionary Genetics Analysis Version 11 Hit paper breakdown → | 2021 | 12767 |
| 7 | Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Hit paper breakdown → | 1993 | 9617 |
| 8 | MEGA2: molecular evolutionary genetics analysis software Hit paper breakdown → | 2001 | 5715 |
| 9 | Prospects for inferring very large phylogenies by using the neighbor-joining method Hit paper breakdown → | 2004 | 4437 |
| 10 | MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences Hit paper breakdown → | 2008 | 3031 |
| 11 | Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G+C-content biases. Hit paper breakdown → | 1992 | 1782 |
| 12 | Molecular Evolutionary Genetics Analysis (MEGA) for macOS Hit paper breakdown → | 2019 | 1200 |
| 13 | MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers Hit paper breakdown → | 1994 | 1013 |
| 14 | MEGA: Molecular Evolutionary Genetics Analysis, Version 1.02. Hit paper breakdown → | 1995 | 925 |
| 15 | 2003 | 493 | |
| 16 | Estimating divergence times in large molecular phylogenies Hit paper breakdown → | 2012 | 478 |
| 17 | MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing Hit paper breakdown → | 2024 | 364 |
| 18 | 2012 | 272 | |
| 19 | 2002 | 238 | |
| 20 | 2005 | 219 |
About Koichiro Tamura
Koichiro Tamura is a scholar working on Molecular Biology, Genetics, Plant Science, Insect Science and Paleontology, having authored 78 papers that have together received 211.0k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (35 papers), Genetic diversity and population structure (28 papers), Evolution and Genetic Dynamics (17 papers), Evolution and Paleontology Studies (6 papers), RNA and protein synthesis mechanisms (6 papers), Chromosomal and Genetic Variations (6 papers), Insect Resistance and Genetics (5 papers) and Insect-Plant Interactions and Control (5 papers). The work is most often cited by research in Plant Science (59.9k citations), Parasitology (10.1k citations), Ecology (40.8k citations), Endocrinology (7.5k citations) and Horticulture (1.4k citations). Koichiro Tamura has collaborated with scholars based in Japan, United States and Saudi Arabia. Frequent co-authors include Sudhir Kumar, Glen Stecher, M Nei, Alan Filipski, Daniel S. Peterson, Joel T. Dudley, Daniel G. Peterson, Michael Li, Masatoshi Nei and Ingrid B. Jakobsen. Their work appears in journals such as Molecular Biology and Evolution, Bioinformatics, Biochemical Genetics, Genetics and BMC Genomics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.